| Presentation # | 19 |
| Session: | ASR IV |
| Location: | Kallirhoe Hall |
| Session Time: | Friday, December 21, 13:30 - 15:30 |
| Presentation Time: | Friday, December 21, 13:30 - 15:30 |
| Presentation: |
Poster
|
| Topic: |
Speech recognition and synthesis: |
| Paper Title: |
RAPID SPEAKER ADAPTATION OF NEURAL NETWORK BASED FILTERBANK LAYER FOR AUTOMATIC SPEECH RECOGNITION |
| Authors: |
Hiroshi Seki, Toyohashi University of Technology, Japan; Kazumasa Yamamoto, Chubu University, Japan; Tomoyosi Akiba, Toyohashi University of Technology, Japan; Seiichi Nakagawa, Chubu University, Japan |
| Abstract: |
Deep neural networks (DNN) have achieved significant success in the field of automatic speech recognition. Previously, we proposed a filterbank-incorporated DNN which takes power spectra as input features. This method has a function of VTLN (Vocal tract length normalization) and fMLLR (feature-space maximum likelihood linear regression). The filterbank layer can be implemented by using a small number of parameters and is optimized under a framework of backpropagation. Therefore, it is advantageous in adaptation under limited available data. In this paper, speaker adaptation is applied to the filterbank-incorporated DNN. By applying speaker adaptation using 15 utterances, the adapted model gave a 7.4% relative improvement in WER over the baseline DNN at a significance level of 0.005 on CSJ task. Adaptation of filterbank layer also showed better performance than the other adaptation methods; singular value decomposition (SVD) based adaptation and learning hidden unit contributions (LHUC). |